A streamlined, machine learning-derived approach to risk-stratification in heart failure patients with secondary tricuspid regurgitation.

ABSTRACT:

Aims

Secondary tricuspid regurgitation (sTR) is the most frequent valvular heart disease and has a significant impact on mortality. A high burden of comorbidities often worsens the already dismal prognosis of sTR, while tricuspid interventions remain underused and initiated too late. The aim was to examine the most powerful predictors of all-cause mortality in moderate and severe sTR using machine learning techniques and to provide a streamlined approach to risk-stratification using readily available clinical, echocardiographic and laboratory parameters.

Methods and results

This large-scale, long-term observational study included 3359 moderate and 1509 severe sTR patients encompassing the entire heart failure spectrum (preserved, mid-range and reduced ejection fraction). A r

SUBMITTER: Heitzinger G 

PROVIDER: S-EPMC10125224 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature